class_docstr: input: [b x 16 x 64 x in_planes]
  output: probability sequence: [b x T x num_classes]
code: def __init__(
      self, in_channels, out_channels, sDim, attDim, max_len_labels
  ):
      super(AttentionRecognitionHead, self).__init__()
      self.num_classes = out_channels  # this is the output classes. So it includes the <EOS>.
      self.in_planes = in_channels
      self.sDim = sDim
      self.attDim = attDim
      self.max_len_labels = max_len_labels
  
      self.decoder = DecoderUnit(
          sDim=sDim,
          xDim=in_channels,
          yDim=self.num_classes,
          attDim=attDim,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import sys
  import paddle
  from paddle import nn
  from paddle.nn import functional
label_desc: Use image node_id 1 to create a new AttentionRecognitionHead object from inherited base classes: nn with example: obj = AttentionRecognitionHead(in_channels, out_channels, sDim, attDim, max_len_labels)